Group analysis system and group analysis equipment

ABSTRACT

Face-to-face detection by infrared-ray communication is effective in grasping interaction between persons. However the problem here is that infrared rays have a high directivity and detection fails unless the persons face each other right in front. Sensor signals having a high directivity and sensor signals having a low directivity are obtained from a sensor terminal (TR) carried by a person. Firstly, information on relative position is obtained with a sensor (TRIR) of infrared rays or the like having a high directivity and an initial group is formed at an application server (AS). A feature amount such as sound that has a low directivity and can sense surrounding environmental information is extracted from among the terminals (TRs) belonging to the initial group by personal feature extraction (ASIF), correlation with terminals (TRs) not belonging to a group is obtained, and thereby whether or not those terminals (TRs) belong to an identical group is judged.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application of U.S. application Ser.No. 12/000,141 filed on Dec. 10, 2007, which issued as U.S. Pat. No.7,656,274 on Feb. 2, 2010. The present application claims priority fromU.S. application Ser. No. 12/000,141 filed on Dec. 10, 2007, whichclaims priority from Japanese application 2007-006151 filed on Jan. 15,2007, the content of which is hereby incorporated by reference into thisapplication.

FIELD OF THE INVENTION

The present invention relates to a technology of detecting relativepositions of terminals and carrying out group analysis with sensorsignals sent from the terminals carried by persons.

BACKGROUND OF THE INVENTION

It is possible to further accelerate a project by: keeping track of theprogress of business and recording the business contents of individuals(members) in the project; and thereby visualizing and sharing theproblems arising during the management of the project. Further, as amember, the member can easily look back his own business afterward byvisualizing daily business and hence the difference from the businesspolicy that has been determined by himself/herself becomes obvious andcan be used as an indicator for the improvement of himself.

In particular, a person behaves variously in his daily life by havingsome relationship with another person. In this regard, it is veryeffective from the viewpoint of grasping the movement of an individualto know the relationship among plural persons by analyzing a group suchas a project. In view of such background, for example JP-A No.2004-028642 discloses a technology wherein a terminal carried by aperson: computes relative positions between terminals through the datacommunication with another terminal by using an identifier (ID); furtherkeeps track of the absolute position of the person himself by using theglobal positioning system (GPS); displays the two pieces of informationon a map; and thereby groups the terminals.

SUMMARY OF THE INVENTION

It is possible to group plural persons having terminals on the basis ofpositional information by conventional technologies. However, behaviorof a person is complicated and, merely with the positional informationalone, it is limited to: accurately extract human relations whereininteraction takes place actually; and obtain useful information. Forexample, the existence of plural persons nearby may be only accidentaland inversely there might be some occasions where distant plural personstalk to each other.

In the meantime, face-to-face detection by infrared-ray communication iseffective in grasping interaction between persons. However the problemhere is that infrared rays have a high directivity and detection failsunless the persons face each other right in front.

An object of the present invention is to provide a group analysis systemand group analysis equipment capable of detecting that interaction takesplace among plural persons even when they do not face each other rightin front.

In order to attain the above object, the present invention is a groupanalysis system comprising a plurality of sensor terminals and acontroller connected to the plural sensor terminals, wherein firstlyeach of the sensor terminals has a first sensor to detect relativepositions and a second sensor to acquire surrounding environmentalinformation. Then the controller acquires sensor signals from the firstand second sensors in each of the sensor terminals through aninput-output unit and, in an analyzer, sets an initial group of thesensor terminals with the sensor signals of the first sensor, andextracts the feature quantity of the initial group from the sensorsignals of the second sensor received from each of the sensor terminalsconstituting the initial group. The group to which a sensor terminal notbelonging to the initial group belongs is decided by comparing thefeature quantity of the initial group with the feature quantity,corresponding to the feature quantity of the initial group, extractedfrom sensor signals of the second sensor of a sensor terminal allegedlyhaving no initial group to belong to.

That is, in the present invention, target persons are made to havesensor terminals and group analysis is carried out from relativeinformation among the sensor terminals. In the analysis, signals from asensor having a strong directivity and a sensor having a weakdirectivity are used preferably. Firstly, relative positionalinformation among persons is acquired with sensor signals obtainablefrom a sensor having a strong directivity and an initial group isformed. A feature quantity that surrounding environmental informationcan sense is extracted from among the sensor terminals belonging to theinitial group with sensor signals obtainable from a sensor having a weakdirectivity. Then whether or not the sensor terminals belong to anidentical group is judged by acquiring correlation with a sensorterminal not belonging to the group.

From the above, it is possible to extract interaction among persons notby sensing a site where a person exists (absolute positioning) but bysensing relative positional information of sensor terminals.

By the present invention, it is possible to detect terminalsconstituting a group even in the case of not facing each other right infront by forming the group at two stages on the basis of thecharacteristics of the sensor signals of each terminal. Further,although, in the case of not classifying into two stages, the frequencyof comparison between terminals is the factorial of the number of theterminals since all the terminals must be subjected to analysis, it ispossible to reduce the analysis frequency by dividing into two stages.

By so doing, it is possible to carry out accurately and quickly groupanalysis for the visualization of daily business such as behaviormeasurement of blue-collars and business measurement of white-collars.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view showing the whole configuration from terminals totransmit sensor signals to an application unit to display group analysisresults according to the first embodiment of the present invention;

FIG. 2 is a view showing the scenes of operations up to the time when auser (US) receives information on group analysis by operating a client 1(CL1) in the first embodiment;

FIG. 3 is a view showing the sequence of sensor signals while they aretransmitted from a terminal 1 (TR1) to a user (US) in the firstembodiment;

FIG. 4 is a view showing a method for sensing signals from a sensor inthe first embodiment;

FIG. 5 is a flowchart showing group analysis to analyze identity of afacing person from sensor signals sent from a terminal in the firstembodiment;

FIG. 6 is a table showing judging functions for sensing typesrespectively in the first embodiment;

FIG. 7 is a table showing a sensor database (SSDBSE) to store sensorsignals in the first embodiment;

FIG. 8 is a table showing a group/person database (ASDBGP) to store theresults of group analysis in the first embodiment;

FIG. 9 is a chart showing sensor signals obtained by integrating featurequantities of groups/persons in the judging function 1 in the firstembodiment;

FIG. 10 is a chart showing sensor signals individually showing a featurequantity of each group/person in the judging function 1 in the firstembodiment;

FIG. 11 is a chart showing sensor signals obtained by integratingfeature quantities of groups/persons in the judging function 2 in thefirst embodiment;

FIG. 12 is a chart showing sensor signals individually showing a featurequantity of each group/person in the judging function 2 in the firstembodiment;

FIG. 13 is a table showing a group/person feature quantity database(ASDBGPF) to store feature quantities for carrying out group analysis bythe judging function 3 in the first embodiment;

FIG. 14 is a view showing the whole configuration from terminals totransmit sensor signals to an application unit to display group analysisresults according to the second embodiment of the present invention; and

FIG. 15 is a view showing the sequence of sensor signals while they aretransmitted from a terminal 1 (TR1) to a user (US) in the secondembodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments according to the present invention are hereunder explainedin detail in reference to drawings.

Embodiment 1

FIG. 1 is a view showing a whole group analysis system from sensorterminals to transmit sensor signals (sensing data) to an applicationunit to display group analysis results according to the firstembodiment. In the present embodiment, a target person is made to carrya sensor terminal and group analysis is carried out with a high degreeof accuracy from relative information between sensor terminals carriedby persons without specifying the locations of the persons.

In FIG. 1, a storage server (SS), an application server (AS), clients(CL1 and CL2, hereunder referred to as CL), and base stations (BA1 andBA2, hereunder referred to as BA) are connected to each other through alocal area network (LAN) and the base stations (BAs) transmit andreceive data wirelessly to and from terminals (TR1 to TR7, hereunderreferred to as TR).

The terminals (TRs) are sensor terminals to carry out sensing at thesites and are possessed by target persons. Each terminal (TR) comprisesa sender/receiver unit (TRSR), a sensing unit (TRSE), an input-outputunit (TRIO), a control unit (TRCO), a recording unit (TRME), and aninside information unit (TRIN). In the present embodiment, the sensingunit (TRSE) contains plural sensors therein, infrared (TRIR) is used fora first sensor to detect relative location information like face-to-facedetection, and a person carrying the terminal is subjected toface-to-face detection with the infrared sensor. Consequently, it isdesirable to carry a terminal (TR) so as to be visible from an opponent.An example is to hang a terminal (TR) from a neck.

A terminal (TR) transmits sensor signals that are a value sensed at thesensing unit including the infrared sensor as the first sensor from thesender/receiver unit (TRSR) to a base station (BA). The sender/receiverunit (TRSR) sends and receives data to and from the base station (BA) bywireless communication based on, for example, IEEE802.15.4 Standard. Asthe transmission methods, there are a method of sending sensor signalsin response to a control command sent from the base station (BA), amethod of sending sensor signals to the base station (BA) periodically,and a method of sending sensor signals to the base station (BA)immediately after receiving sensor signals. Further, the change ofcontrol information regarding the terminal (TR) and output to an outputdevice in the input-output unit (TRIO) are carried out in response to acontrol command sent from the base station (BA). Furthermore, itemsselected by an input device such as a button at the input-output unit(TRIO) are sent to the base station (BA) as control commands.

As stated above, the sensing unit (TRSE) senses a site. As the sensorsused for sensing, in addition to infrared (TRIR) as the first sensor,there are temperature (TRTE), a microphone (TRMI), acceleration (TRAC),and illuminance (TRIL) as the second sensor to extract a featurequantity as surrounding environmental information. Further, by using aterminal of the external input (TROU), it is possible to cope with thecase of adding a new sensor such as a gravity sensor, an image sensor,or the like. With those sensors, it is possible to obtain information onsurrounding environment wherein a person carrying the sensor exists, inaddition to face-to-face detection as the relative position detection ofa person carrying a terminal (TR) of an infrared sensor. Here, as itwill be described later in detail, by extracting a prescribed featurequantity from such environmental information, it is possible to finallydecide the group to which the person possessing the terminal (TR)belongs even though it has been impossible to decide the initial groupto which the person belongs by the face-to-face detection.

The input-output unit (TRIO) comprises an input device such as a buttonand an output device such as a liquid crystal display. The unit obtainsinformation which the target person requests and displays sensedinformation. Here, a touch panel that is a device formed by integratingan input device and an output device may also be used.

The control unit (TRCO) has a CPU (Central Processing Unit) and carriesout programs stored in the recording unit (TRME). The control unit(TRCO) processes the acquisition timing of sensor information, theanalysis of sensor information, and the timing of transmission andreception to and from the base station (BA).

The recording unit (TRME) comprises a hard disk, a memory, or anexternal recording unit such as an SD (Secure Digital) card and storesprograms and sensed sensor signals.

The inside information unit (TRIN) contains information on the terminal(TR) and stores the information in a similar recording unit. Therecording unit (TRME) and the inside information unit (TRIN) may begenerically called a memory unit in some cases. The information of theterminal (TR) includes terminal information (TRTR) as terminal specificinformation such as a battery monitor (TRBA) to show the remainingamount in the battery of the terminal, a clock (TRTI), and addresses.The battery monitor (TRBA) records the remaining amount of an electricpower supply. The clock (TRTI) stores current time. The current time issent from the base station (BA) periodically.

Successively, a base station (BA) is explained. A base station (BA) isplaced in an area from which information is expected to obtain, andthereby receives sensor signals wirelessly sent from a terminal (TR) inthe area and transmits the sensor signals to a storage server (SS)through a LAN. The base station (BA) comprises a sender/receiver unit(BASR), a control unit (BACO), a recording unit (BAME), an input-outputunit (BAIO), and an inside information unit (BAIN).

The sender/receiver unit (BASR) transmits and receives data to and froma terminal (TR). The sender/receiver unit (BASR) transmits a controlcommand to a terminal (TR) as a transmission method and receives sensorsignals from a terminal (TR) periodically or receives sensor signalstransmitted from a terminal (TR) immediately after various sensors ofthe terminal (TR) detect the sensor signals. Further, thesender/receiver unit (BASR) acquires information from the storage server(SS) and sends the results obtained from the storage server (SS) to aterminal (TR) by the control command sent from the terminal (TR).Furthermore, the unit sends items selected by an input device in theinput-output unit (TRIO) of a terminal (TR) as the control command tothe base station (BA) and the storage server (SS). In addition, the unitchanges screen display of an output device of the input-output unit(TRIO) by the control command sent from the storage server (SS) and theterminal (TR).

The control unit (BACO) is equipped with a CPU and runs programs storedin the recording unit (BAME). The unit processes the acquisition timingof sensor information, the analysis of the sensor information, thetransmission/reception timing of the base station (BA) and the storageserver (SS).

The recording unit (BAME) comprises a hard disk, a memory, or anexternal recording unit such as an SD card, and stores programs andsensed sensor signals.

The input-output unit (BAIO) comprises an input device such as a buttonand an output device such as a liquid crystal display. The unit displaysinformation on the situation in a target area and sensed information.Here, a touch panel that is a device formed by integrating an inputdevice and an output device may also be used.

The inside information unit (BAIN) contains information on the basestation (BA) and the information is stored in a similar recording unit.The recording unit (BAME) and the inside information unit (BAIN) aregenerically called a memory unit in some cases. In the insideinformation unit (BAIN), as the information of the base station (BA),there are a clock (BATI) to show time and base station information(BABA) as information intrinsic to the base station. The clock (BATI)contains current time. The clock is periodically connected to an NTP(Network Time Protocol) server, adjusts the time, and further adjuststhe time of a terminal (TR) periodically.

Here, a local area network (LAN) is a network to connect base stations(BAs), a storage server (SS), an application server (AS), and clients(CLs) to each other as stated above.

Successively, the storage server (SS) stores sensor information sentfrom a base station (BA) and sends sensor signals (sensing data) to theapplication server (AS) on the basis of the request of the desiredsensor signals (sensing data) from the application server (AS). Further,the server receives a control command from a base station (BA) andtransmits the results obtained from the control command to the basestation (BA). The storage server (SS) is an ordinary computer systemcomprising a database unit (SSDB), a control unit (SSCO), asender/receiver unit (SSSR), an input-output unit (SSIO), and arecording unit (SSME).

The database unit (SSDB) stores sensor signals sent from a base station(BA). Further, the unit stores a method for treating a control commandfrom a base station (BA).

The control unit (SSCO) is equipped with a CPU and runs programs storedin the recording unit (SSME). The unit controls the database unit (SSDB)and processes information sent from the application server (AS) and abase station (BA).

The sender/receiver unit (SSSR) exchanges transmission/reception with abase station (BA). The unit receives sensor signals (sensing data) sentfrom a base station (BA) and also transmits the sensor signals to theapplication server (AS). Further, when the unit receives a controlcommand from a base station (BA), the unit transmits the resultsselected from the database unit (SSDB) to the base station (BA).

The sender/receiver unit (SSSR) transmits a control command to aterminal (TR), receives sensor signals from a terminal (TR)periodically, and transmits the sensor signals from a terminal (TR) tothe storage server (SS) immediately after receiving the sensor signalsfrom the terminal (TR). Further, the unit requests to acquireinformation to the storage server (SS) and transmits the informationresultantly acquired from the storage server (SS) to a terminal (TR) inresponse to the control command sent from the terminal (TR).

The input-output unit (SSIO) comprises an input device such as a buttonand an output device such as a liquid crystal display. The unit displaysinformation on the situation in a target area and sensed information.Here, a touch panel that is a device formed by integrating an inputdevice and an output device may also be used.

The recording unit (SSME) comprises a hard disk, a memory, or anexternal recording unit such as an SD card, and stores programs andsensed sensor signals. The recording unit (SSME) and the database unit(SSDB) may be generically called a memory unit in some cases.

Meanwhile, the application server (AS) is a server as a controller ofthe system to carry out group analysis from sensor signals stored in thestorage server (SS) and comprises an ordinary computer system like theaforementioned storage server (SS). The application server (AS)comprises a group analysis unit (ASGA), a control unit (ASCO), arecording unit (ASME), a sender/receiver unit (ASSR), an input-outputunit (ASIO), and a database unit (ASDB).

The group analysis unit (ASGA) is a section to analyze a group in termsof time by analyzing sensor signals. The group analysis unit (ASGA)processes initial group detection (ASIG), group matching (ASGM), groupfeature extraction (ASGF), and personal feature extraction (ASIF). Thegroup analysis unit (ASGA) comprises ordinary programs and the programsare run with the control unit (ASCO) that is explained below.

The control unit (ASCO) is equipped with a CPU and runs programs andothers stored in the recording unit (ASME). That is, the unit processesrequest to acquire data in the storage server (SS), the aforementionedgroup analysis, the management of the analysis results, etc.

The recording unit (ASME) comprises a hard disk, a memory, or anexternal recording unit such as an SD card, and stores theaforementioned programs, sensed sensor signals, and the analysisresults.

The sender/receiver unit (ASSR) acquires data signals from the storageserver (SS) and transmits the data on the basis of analysis resultrequest from a client (CL). The input-output unit (ASIO) comprises aninput device such as a button and an output device such as a liquidcrystal display. The unit displays information on the situation in atarget area and sensed information. Here, a touch panel that is a deviceformed by integrating an input device and an output device may also beused.

The database unit (ASDB) stores the results of analysis carried out inthe group analysis unit (ASGA), namely the control unit (ASCO). Ingeneral, the results are memorized in a hard disk or the like. Therecording unit (ASME) and the database unit (ASDB) may be genericallycalled a memory unit in some cases.

Lastly, a client (CL), on the basis of request from a user, receivesgroup analysis results in the application server (AS), processes data,and displays the results on a screen. The client (CL) is an ordinarypersonal computer (PC) comprising an application unit (CLAP), asender/receiver unit (CLSR), an input-output unit (CLIO), a recordingunit (CLME), a control unit (CLCO), etc.

The application unit (CLAP) processes data, makes a picture, andprovides the data to a user on the basis of request from the user. Theapplication unit (CLAP) includes operation (CLOP), process (CLPR), and ascreen (CLSC), is configured as an ordinary program, is recorded in theafter-mentioned recording unit (CLME), and is operated with theafter-mentioned control unit (CLCO).

The sender/receiver unit (CLSR) transmits request for the analysisresults in the range assigned by a user to the application server (AS)and receives the analysis results from the application server (AS). Theinput-output unit (CLIO) comprises an input device such as a button andan output device such as a liquid crystal display. The unit displaysinformation on the situation in a target area and sensed information.Here, a touch panel that is a device formed by integrating an inputdevice and an output device may also be used.

The recording unit (CLME) comprises a hard disk, a memory, or anexternal recording unit such as an SD card, and stores a main program,sensed sensor signals, and analysis results. The control unit (CLCO) isequipped with a CPU and runs programs stored in the user recording unit(CLME).

FIG. 2 is a view schematically showing the scenes of operations of agroup analysis system according to the first embodiment. A user receivesinformation on group analysis by operating a client (CL).

The screen (CLSC) of the client (CL) is a picture output by the user(US) with an application in the client (CL). The screen (CLSC) shows thepersons who have stayed together in temporal sequence with a graph. Anapplication unit (CLAP) explained earlier of the client (CL) isconnected to an application server (AS) and receives group analysisresults stored in a database (ASDB) through a local area network (LAN).

The application server (AS) is connected to a storage server (SS) andreceives sensor signals stored in a database (SSDB) through the localarea network (LAN). Further, the application server (AS) is connected tothe client (CL) and transmits sensor signals stored in the database(ASDB) through the local area network (LAN).

The storage server (SS) is connected to a base station (BA) and receivessensor signals through the local area network (LAN). The base station(BA) transmits sensor signals to the storage server (SS) through thelocal area network (LAN). The base station (BA) receives sensor signalsfrom a terminal (TR) through a sender/receiver unit (BASR). The terminal(TR) transmits sensor signals to the base station (BA) through asender/receiver unit (TRSR). The terminal (TR) obtains sensor signalswith a sensing unit (TRSE).

Here, although the system of the present embodiment shown in FIGS. 1 and2 is configured so that all of the storage server (SS), the applicationserver (AS), and the client (CL) may be separately installed andconnected to each other through a local area network (LAN), it goeswithout saying that they may be integrally configured. For example, thestorage server (SS) and the application server (AS) as the upper leveldevices may be configured as a computer system/server functioning as anintegrated management device. Further, the client (CL) may be configuredas a terminal unit attached to the application server (AS) in somecases.

FIG. 3 is a diagram showing the sequence of sensor signals while theanalysis results of the sensor signals are transmitted from a terminal(TR) to a user (US) in a group analysis system according to the firstembodiment.

The terminal (TR) receives sensor signals sensed with a sensing unit(TRSE) and transmits the sensed signals to a base station (BA). The basestation (BA) receives the sensor signals from the terminal (TR) andtransmits the acquired sensor signals to a storage server (SS). Thestorage server (SS) receives the sensor signals from the base station(BA) and stores the acquired sensor signals in a database (SSDB) of thestorage server (SS).

An application server (AS) as a controller of a group analysis systemrequests sensor signals to the storage server (SS), carries out groupanalysis with the transmitted sensor signals (sensing data), and storesthe results in a database (ASDB) of the application server (AS). Aclient (CL) requests group analysis results to the application server(AS) when the user (US) activates an application. The client (CL)processes the transmitted analysis results into information desired bythe user, forms a picture, and outputs it to an output device of aninput-output unit (CLIO). The user (US) activates the application ofgroup analysis in the client (CL) and browses figures displayed on thescreen of the client (CL).

The sequence of the system is hereunder described in detail. Firstly, inthe process of sensor signal acquisition (TRGE) in a terminal (TR),information necessary for acquiring sensor signals such as a samplingcycle, acquisition time, etc. is described in a recording unit (TRME) inthe terminal (TR) and the sensor signals are sensed on the basis of theinformation. The sensing is carried out with a sensor located at asensing unit (TRSE) in the terminal (TR). Further, the sensor signalssensed with the sensing unit (TRSE) are recorded in the recording unit(TRME). Time attachment (TRAD) in the terminal (TR) is carried out byrecording the time of a clock (TRTI) as the data acquisition time of thesensed data.

In data transmission (TRSE), sensor signals sensed through the sensorsignal acquisition (TRGE) are transmitted to a base station (BA) througha sender/receiver unit (TRSR). Sensor signals recorded in the recordingunit (TRME) are converted into a transmission format for a base station(BA) stored in the recording unit (TRME) at a control unit (TRCO), andthe sensor signals converted into the transmission format aretransmitted to the base station (BA) through the sender/receiver unit(TRSR). As the transmission format, a format standardized in theaforementioned radio communication is used.

In data reception (BARE) in the base station (BA), sensor signalstransmitted from the sender/receiver unit (TRSR) of the terminal (TR) inthe transmission format for the base station (BA) are received at asender/receiver unit (BASR). Then the received sensor signals are storedin a recording unit (BAME).

In data transmission (BASE), the sensor signals stored in the recordingunit (BAME) are transmitted to a storage server (SS) through thesender/receiver unit (BASR). The sensor signals recorded in therecording unit (BAME) are converted into a transmission format for thestorage server (SS) stored in the recording unit (BAME) at a controlunit (BACO), and the sensor signals converted into the transmissionformat are transmitted to the storage server (SS) through thesender/receiver unit (BASR).

In data reception (SSRE) in the storage server (SS), the sensor signalstransmitted from the sender/receiver unit (BASR) in the base station(BA) in the transmission format for the storage server (SS) are receivedwith a sender/receiver unit (SSSR). Then the received sensor signals arestored in a recording unit (SSME).

In data storage (SSPU), the sensor signals stored in the recording unit(SSME) are converted into a format stored in a database unit (SSDB)stored in the recording unit (SSME) at a control unit (SSCO) and arestored in the database unit (SSDB). A method for storing sensor signalsin the database unit (SSDB) is desirably used as effective queries onthe occasion of after-mentioned search, and there are a sensor signalname, time, a terminal name, a base station name, etc. as the effectivequeries. A series of processes ranging from the sensor signalacquisition (TRGE) to the data storage (SSPU) are carried outperiodically.

Successively, in data request (ASRQ) with an application server (AS),sensor signals recorded in the recording unit (ASME) of the applicationserver (AS) or a recording unit (CLME) of the client (CL) are acquiredfrom a target terminal at a time to acquire data.

In data search (ASSE), the storage server (SS) is searched in responseto the data request (ASRQ). Information necessary for the acquisition ofdata signals, such as the name and the address of the storage server(SS), the name of a database, the name of a table, etc., is described inthe recording unit (ASME). On the occasion of data search (ASSE), thesearch contents are obtained from the data request (ASRQ) andinformation on the database is obtained from the recording unit (ASME),and thereby a command used for the search is produced. The command isconverted into a transmission format for the storage server (SS) storedin the recording unit (ASME) at a control unit (ASCO) and the commandconverted into the transmission format is transmitted to the storageserver (SS) through a sender/receiver unit (ASSR).

In data reception (ASRE), relevant sensor signals transmitted from thedatabase unit (SSDB) in the storage server (SS) are received in responseto the command of the data search (ASSE). The sensor signals receivedwith the sender/receiver unit (ASSR) are recorded in the recording unit(ASME).

In group analysis (ASGA), the program is stored in the recording unit(ASME) and the group analysis (ASGA) is processed in the control unit(ASCO). In the group analysis (ASGA), the program is used for theanalysis of a group and the analysis is carried out with the sensorsignals stored in the recording unit (ASME) through the data reception(ASRE).

In analysis result storage (ASPU), the results of the group analysis(ASGA) are stored in a database unit (ASDB). On the occasion of thestorage, it is desirable to store not only the analysis results but alsoinformation on analysis conditions and thus information proposed uponthe data request (ASRQ) is stored together. The series of processesranging from the data request (ASRQ) to the analysis result storage(ASPU) are carried out periodically.

Successively, in application start (USST), an application is activatedby a user (US). An application is activated, a user (US) selects a startbutton, and thereby a desired event is displayed on a screen.

In data request (CLRQ), information necessary for the display isacquired. A user (US) selects a button of an input-output unit (CLIO) ofa client (CL) and thereby the time of analysis and the target terminalinformation are acquired.

In data search (CLSE), search is requested to the application server(AS) in response to the data request (CLRQ). Information necessary forthe acquisition of data signals, such as the name and the address of theapplication server (AS), the name of a database, the name of a table,etc. is described in a recording unit (CLME). On the occasion of thedata search (CLSE), the search contents are obtained from the datarequest (CLRQ) and information in the database is obtained from therecording unit (CLME), and thereby a command used for the search isproduced. The command is converted into a transmission format for theapplication server (AS) stored in the recording unit (CLME) at a controlunit (CLCO) and the command converted into the transmission format issent to the application server (AS) through a sender/receiver unit(CLSR).

In data reception (CLRE), relevant analysis results transmitted from thedatabase unit (ASDB) in the application server (AS) are received inresponse to the command of the data search (CLSE). The analysis resultsreceived by the sender/receiver unit (CLSR) are stored in the recordingunit (CLME).

In data process (CLDP), only information necessary for display isselected from the analysis results acquired through the data reception(CLRE) and is stored in the recording unit (CLME). In display (CLDI), animage or a picture is produced from the information selected through thedata process (CLDP) on the basis of the display method described in therecording unit (CLME). The produced result is submitted to the user(US). Application end (USEN) represents the termination of applicationby a user (US).

Here, time correction (BATM) in a base station (BA) is carried out inorder to adjust the time of a clock (BATI) in the base station (BA). Thecurrent time is acquired from an NTP server in a local area network(LAN). The process of the time correction (BATM) is carried outperiodically.

In time correction request (BATR), time correction is requested to aterminal (TR) in order to adjust the time of the terminal (TR). In timecorrection (TRTM), the time of a clock (TRTI) is corrected on the basisof the time transmitted from a base station (BA) in response to the timecorrection request (BATR). The processes from the time correctionrequest (BATR) to the time correction (TRTM) are carried outperiodically.

Meanwhile, there are various sampling types in a sensing method with asensor in the present embodiment and one of the examples is shown inFIG. 4. Periodical sensing data sending (SDSF) represents the cycle atwhich sensor signals are transmitted to a base station. Sampling time(SPT) means a time spent for sampling in the periodical sensing datasending (SDSF). Sampling rate (SPR) means the number of sampling in thesampling time (SPT).

Sampling type (ST1) shows the situation wherein sampling is applied tothe whole cycle of the periodical sensing data sending (SDSF).Consequently, the time of the periodical sensing data sending (SDSF) isidentical to the sampling time (SPT). Sampling type (ST2) shows thesituation wherein sampling is applied to apart of the cycle of theperiodical sensing data sending (SDSF). Consequently, the sampling time(SPT) is shorter than the time of the periodical sensing data sending(SDSF).

A method for determining the values of the periodical sensing datasending (SDSF), the sampling time (SPT), and the sampling rate (SPR)stated above varies in accordance with the situation of usage. Theoptimum values are selected in response to an anticipated utilizationtime and an anticipated amount of communication between a terminal (TR)and a base station (BA). For example, assuming that the cycle of theperiodical sensing data sending (SDSF) is 10 seconds, the sampling times(SPTs) of the sampling types (ST1) and (ST2) are 10 and 2 secondsrespectively. On this occasion, the sampling rate (SPR) is set at about50 Hz for example.

Successively, a flowchart of group analysis to analyze the identity ofthe group to which a person belongs from sensor signals sent from aterminal in the present embodiment is shown in FIG. 5. On the occasionof group analysis, the judging functions vary in accordance with thedata acquisition methods shown in FIG. 4 and hence the relevant judgingfunctions are explained below.

As shown in FIG. 6, the sensing type judging functions vary inaccordance with a sensing type; the judging function 1 (JA1) and thejudging function 3 (JA3) are applied in the case of the sensing type 1(ST1), and the judging function 2 (JA2) and the judging function 3 (JA3)are applied in the case of the sensing type 2 (ST2).

Firstly, the judging function 1 (JA1) is explained. The judging function1 (JA1) is applied in the case of the sensing type 1 (ST1) and is ajudging function used when sampling is applied to the whole cycle of theperiodical sensing data sending (SDSF).

In FIG. 5, in initial group detection (ASIG), an initial group isdetected with information that comes from a sensor having a highdirectivity including an infrared sensor as the first sensor and isstored in an after-mentioned sensor database (SSDBSE) shown in FIG. 7.For example, an initial group is formed by carrying out face-to-faceinference (detection and frequency with an infrared sensor) withinfrared rays for a certain period of time. The existence (frequency) offacing is assigned to the group/person database (ASDBGP) shown in FIG.8. Here, as other sensors having a high directivity, there are an imagesensor and others.

In presence of an initial group (ASIGJU), terminals are classified intothe terminals belonging to the initial group and the terminals notbelonging to the initial group. That is, terminals detected as theinitial group are classified into group feature extraction (ASGF) andterminals not detected as the initial group are classified into personalfeature extraction (ASPF). In the group feature extraction (ASGF), afeature amount as the information showing the environment of a site(surrounding environment) is extracted from the sensor signals of aterminal belonging to the same group.

In a feature extraction method, for example, the values of sounds(SSSDs) captured with a microphone (TRMI) as the second sensor areobtained from among the sensor signals of all the terminals detected asthe identical initial group and the averages of the values are obtainedin temporal sequence. In FIG. 9, the averages of the values of sounds(SSSDs) are aligned in temporal sequence for each of the group 1(ASGPT11G), the group 2 (ASGPT12G), and the group 3 (ASGPT13G). It isdesirable that the second sensor used for the feature extractionproduces sensor signals that can be environmental information showingsurrounding environment and it is also possible to obtain and use notonly the values of sounds (SSSDs) but also temperature (TRTE),illuminance (TRIL), or a feature amount (zero-cross, energy, variance,or a regression coefficient) obtained from sounds (SSSDs), temperature(TRTE), or illuminance (TRIL).

Further, it is also possible to use sensor signals from each of theterminals belonging to a group as shown in FIG. 10, instead ofintegrating them into one group. In FIG. 10, sensor signals of eachperson in each group are shown. That is, the persons are a person of thegroup 1 (ASGPT11P1), another person of the group 1 (ASGPT11P2), a personof the group 2 (ASGPT12P1), another person of the group 2 (ASGPT12P2), aperson of the group 3 (ASGPT13P1), and another person of the group 3(ASGPT13P2).

In the personal feature extraction (ASPF), a feature amount showing theenvironment of a site is extracted from sensor signals of a terminaljudged as not belonging to any group by the initial group detection(ASIG). On the occasion of the extraction, it is necessary to use thesame feature extraction method as the method used for the feature amountin the group feature extraction (ASGF) since the feature amount iscompared with the group feature extraction (ASGF) afterward.

In a feature extraction method, for example, the values of sounds(SSSDs) captured with a microphone (TRMI) are obtained from among sensorsignals of a terminal and are aligned in temporal sequence. The person 4(ASGPT14) in FIG. 9 is the case where the values of sounds (SSSDs) arealigned in temporal sequence. It is desirable that the second sensorused for the feature extraction produces sensor signals that can showsurrounding environment and it is also possible to obtain and use notonly the values of sounds (SSSDs) but also temperature (TRTE),illuminance (TRIL), or a feature amount (zero-cross, energy, variance,or a regression coefficient) obtained from sounds (SSSDs), temperature(TRTE), or illuminance (TRIL).

In group matching (ASGM), an inner volume or a distance is computed forexample in order to obtain the similarity between the group featureextraction (ASGF) and the personal feature extraction (ASPF). Athreshold value is set beforehand and, if a value obtained by thecomputation falls within the range of the threshold value, it is judgedto be the same group. Then the result is assigned to the group/persondatabase (ASDBGP) shown in FIG. 8. Further, it is also possible to getsimilarity among persons or groups in order to form a bigger group. Forexample, the person 4 (ASGPT14) shown in FIG. 9 is judged to have a highsimilarity with the group 3 (ASGPT13G) as a result of group matching(ASGM) and hence is decided to be included in the group 3.

The group analysis is continued until no matching occurs at the matchingend (ASENJU) in the sequence shown in FIG. 5. When the analysis iscontinued, the process goes back to the entry side of the presence ofthe initial group (ASIGJU). Otherwise, it is also possible to count thefrequency and repeatedly return to the entry side of the presence of aninitial group (ASIGJU) until the count reaches a prescribed number.

Successively, the judging function 2 (JA2) is explained. The judgingfunction 2 (JA2) is applied in the case of the sensing type 2 (ST2) andis a judging function used when sampling is applied only to a part ofthe cycle of the periodical sensing data sending (SDSF).

In the sequence shown in FIG. 5, the initial group detection (ASIG) isthe same as the case of the judging function 1. The presence of aninitial group (ASIGJU) is also the same as the case of the judgingfunction 1.

In the group feature extraction (ASGF), a feature amount showing theenvironment of a site is extracted from sensor signals showing theenvironmental information of terminals belonging to an identical group.In a feature extraction method, for example, the values of sounds(SSSDs) captured with a microphone (TRMI) are obtained from among thesensor signals of all the terminals detected as the same initial groupand aligned in temporal sequence. An example thereof is shown in FIG. 11and, in the figure, the averages of the values of sounds (SSSDs) arealigned in temporal sequence for the group 1 (ASGPT21G), the group 2(ASGPT22G), and the group 3 (ASGPT23G), respectively. If there areoverlapping portions, the average of the values of the relevant sensorsis obtained. It is desirable that the sensor used for the featureextraction produces sensor signals that can show surrounding environmentand it is also possible to obtain and use not only the values of sounds(SSSDs) but also temperature (TRTE), illuminance (TRIL), or a featureamount (zero-cross, energy, variance, or a regression coefficient)obtained from sounds (SSSDs), temperature (TRTE), or illuminance (TRIL).

Further, it is also possible to not only use sensor signals byintegrating them into a group as shown in FIG. 11 but also use sensorsignals from terminals belonging to a group. FIG. 12 shows sensorsignals of each person in each group. The persons are a person of thegroup 1 (ASGPT21P1), another person of the group 1 (ASGPT21P2), a personof the group 2 (ASGPT22P1), another person of the group 2 (ASGPT22P2), aperson of the group 3 (ASGPT23P1), and another person of the group 3(ASGPT23P2).

Meanwhile, in the personal feature extraction (ASPF), a feature amountshowing the environment of a site is extracted from sensor signals asenvironmental information of a terminal not belonging to any group. Onthe occasion of the extraction, it is necessary to use the same featureextraction method as the method used for the feature amount in the groupfeature extraction (ASGF) since the feature amount is compared with thegroup feature extraction (ASGF) afterward.

In a feature extraction method, for example, the values of sounds(SSSDs) captured with a microphone (TRMI) are obtained from among sensorsignals of terminals and are aligned in temporal sequence. The person 4(ASGPT24) in FIG. 11 is the case where the values of sounds (SSSDs) arealigned in temporal sequence. It is desirable that the second sensorused for the feature extraction produces sensor signals that can showsurrounding environmental information and it is also possible to obtainand use not only the values of sounds (SSSDs) but also temperature(TRTE), illuminance (TRIL), or a feature amount (zero-cross, energy,variance, or a regression coefficient) obtained from sounds (SSSDs),temperature (TRTE), or illuminance (TRIL).

The group matching (ASGM) is the same as the case of the judgingfunction 1. For example, the person 4 (ASGPT24) in FIG. 11 is judged tohave a high similarity with the group 3 (ASGPT23G) as a result of groupmatching (ASGM) and hence is decided to be included in the group 3.

Matching end (ASENJU) is the same as the case of the judging function 1.

Successively, the judging function 3 (JA3) is explained. The judgingfunction 3 (JA3) is a judging function that can be used in both thecases of the sensing type 1 (ST1) and the sensing type 2 (ST2). In thejudging function, feature amounts in the whole range are obtained andjudgment is done with the feature amounts.

The initial group detection (ASIG) is the same as the case of thejudging function 1. The presence of an initial group (ASIGJU) is alsothe same as the case of the judging function 1.

In the group feature extraction (ASGF), a feature amount showing theenvironment of a site is extracted from sensor signals of terminalsbelonging to an identical group. In the feature extraction method, forexample, the average of sounds, the maximal value of sounds, the minimumvalue of sounds, and the dispersion of sounds of terminals belonging toa group, or the average of sounds, the maximal value of sounds, theminimum value of sounds, and the dispersion of sounds in a group areobtained. The results are assigned to the group/person feature quantitydatabase (ASDBGPF) in FIG. 13.

The values of the total (ASGPT31G), the total (ASGPT32G), and the total(ASGPT33G) in the group/person feature quantity database (ASDBGPF) shownin FIG. 13 are obtained by assigning the feature amounts for the groups,respectively. Further, the values of the person (ASGPT31P1), the person(ASGPT31P2), the person (ASGPT32P1), the person (ASGPT32P2), the person(ASGPT33P1), and the person (ASGPT33P2) shown in FIG. 13 are the featureamounts of the persons belonging to the relevant groups, respectively.It is desirable that the sensor used for the feature extraction producessensor signals that can show surrounding environment and it is alsopossible to obtain and use not only the values of sounds (SSSDs) butalso temperature (TRTE), illuminance (TRIL), or a feature amount(zero-cross, energy, variance, or a regression coefficient) obtainedfrom sounds (SSSDs), temperature (TRTE), or illuminance (TRIL).Otherwise, it is also possible to obtain the above values by subjectingsensor signals to first differentiation or second differentiation.

In the personal feature extraction (ASPF), a feature amount showing theenvironment of a site is extracted from sensor signals of a terminal notbelonging to any group. On the occasion of the extraction, it isnecessary to use the same feature extraction method as the method usedfor the feature amount in the group feature extraction (ASGF) since thefeature amount is compared with the group feature extraction (ASGF)afterward. In the feature extraction method, for example, the average ofsounds, the maximal value of sounds, the minimum value of sounds, andthe dispersion of sounds are obtained. The results are assigned to thegroup/person feature quantity database (ASDBGPF).

The value of the person 4 (ASGPT34) shown in FIG. 13 is the featureamount of the person. It is desirable that the sensor used for thefeature extraction produces sensor signals that can show surroundingenvironment and it is also possible to obtain and use not only thevalues of sounds (SSSDs) but also temperature (TRTE), illuminance(TRIL), or a feature amount (zero-cross, energy, variance, or aregression coefficient) obtained from sounds (SSSDs), temperature(TRTE), or illuminance (TRIL). Otherwise, it is also possible to obtainthe above values by subjecting sensor signals to first differentiationor second differentiation.

Here, the group matching (ASMG) and the matching end (ASENJU) in thesequence shown in FIG. 5 in the judging function 3 are the same as thecase of the judging function 1.

Meanwhile, the sensor database (SSDBSE) is a database to store sensorsignals such as the aforementioned infrared sensor information and anexample thereof is shown in FIG. 7. The time (SSTI) represents the timewhen sensor signals are acquired. The content (SSCD) represents sensingvalues of sensor signals. The time stamp (SSTS) represents the time ofacquisition at a terminal (TR) or a base station (BA). The terminal ID(SSTD) represents the ID of a terminal (TR) subjected to sensing. Theinfrared (SSID) represents a sensing value by infrared (TRIR). Thetemperature (SSTE) represents a sensing value by temperature (TRTE). Thesound (SSSD) represents a sensing value of a microphone (TRMI). Theacceleration (SSAD) represents a sensing value by acceleration (TRAC).The illuminance (SSLD) represents a sensing value by illuminance (TRIL).

The group/person database (ASDBGP) is a database to store the results ofgroup analysis and an example thereof is shown in FIG. 8. The terminal[name] (ASTD) represents a terminal number. In the time (ASTI), a samenumber is given to each group regarded as the same group as a result ofgroup analysis for each time.

FIG. 9 shows an example of sensor signals produced by integratingfeature amounts for each of the groups. The values are aligned intemporal sequence and the group 1 (ASGPT11G), the group 2 (ASGPT12G),the group 3 (ASGPT13G), and the person 4 (ASGPT14) are shown in theorder from the top.

FIG. 10 shows an example of sensor signals individually showing featureamounts of each person in each of the groups. The values are aligned intemporal sequence and a person of the group 1 (ASGPT11P1), anotherperson of the group 1 (ASGPT11P2), a person of the group 2 (ASGPT12P1),another person of the group 2 (ASGPT12P2), a person of the group 3(ASGPT13P1), another person of the group 3 (ASGPT13P2), and the person 4(ASGPT14) are shown in the order from the top.

FIG. 11 shows an example of sensor signals produced by integratingfeature amounts for each of the groups. The values are aligned intemporal sequence and the group 1 (ASGPT21G), the group 2 (ASGPT22G),the group 3 (ASGPT23G), and the person 4 (ASGPT24) are shown in theorder from the top.

FIG. 12 shows an example of sensor signals individually showing featureamounts of each person in each of the groups. The values are aligned intemporal sequence and a person of the group 1 (ASGPT21P1), anotherperson of the group 1 (ASGPT21P2), a person of the group 2 (ASGPT22P1),another person of the group 2 (ASGPT22P2), a person of the group 3(ASGPT23P1), another person of the group 3 (ASGPT23P2), and the person 4(ASGPT24) are shown in the order from the top.

The group/person feature quantity database (ASDBGPF) is a database tostore feature amounts for group analysis and an example thereof is shownin FIG. 13. The feature amount (ASFE) means feature amounts for groupanalysis. The feature amounts may be obtained from sensor signals andthe examples of the feature amounts are the average of sounds (ASFE1),the maximal value of sounds (ASFE2), the minimum value of sounds(ASFE3), and the dispersion of sounds (ASFE4). When an effective featureamount is found, the feature amount may be added to the feature amounts(ASFE).

The feature amounts of each of the groups and each of the persons arestored as shown in the column of the group/person (ASGP). When personsbelong to a group, the sensor signals are assembled in the group. In thecase of the group 1 (ASGPT31) for example, the feature amounts of thetotal (ASGPT31G) are stored from the total of the sensor signals and thefeature amounts of the person (ASGPT31P1) and the person (ASGPT31P2) arestored from the sensor signals of each of the persons belonging to thegroup. In this way, the feature amounts of the total and the persons ofa group are stored for each group. Further, when a person does notbelong to a group, the feature amounts of the person himself are stored.An example thereof is the person (ASGPT34).

In Embodiment 1 explained above, it is possible to detect terminals(TRs) even without face-to-face contact by grouping the terminals (TRs)in two stages in accordance with the features of sensor signals of thefirst and second sensors obtained from the terminals (TRs) andsubjecting the sensor signals to group analysis. Further, with regard tothe frequency of the comparison of terminals in group analysis, althoughthe frequency of analysis is the factorial of the number of theterminals since the analysis has to be applied to all the terminals inthe case of not grouping the terminals in two stages, it is possible toreduce the frequency of the analysis by grouping the terminals in twostages.

Embodiment 2

The second embodiment is a group analysis system that allows periodicaltime synchronization of terminals. When group analysis is carried outwith signals sensed at terminals (TRs), since the analysis is carriedout with signals from plural terminals (TRs), it is desirable to obtainthe signals of as close time as possible. The reason is that, althoughno problem occurs when the time of periodical sensing data sending(SDSF) is the same as sampling time (SPT), the problem arising when thetime of periodical sensing data sending (SDSF) is different fromsampling time (SPT) is that signals of the same time cannot be obtained.As a result, analysis accuracy is not likely to improve. A systemcapable of coping with the problem is provided in the second embodiment.

The group analysis system of the second embodiment shown in FIG. 14 isconfigured by modifying a part of the whole system ranging fromterminals to transmit sensor signals to an application to display groupanalysis results shown in FIG. 1. The modification is the addition ofsynchronization (BASY) to the inside information unit (BAIN) in a basestation (BA). The synchronization (BASY) represents the process ofsynchronizing the clock time of plural terminals (TRs) and the clocktime when signal data are acquired from terminals (TRs) is synchronizedperiodically.

FIG. 15 is a view showing the sequence of sensor signals while they aretransmitted from a terminal (TR) to a user (US) in the second embodimentand a part of the view in FIG. 3 is modified. The modification is theaddition of time synchronization (BATS) to the processes in a basestation (BA). In the time synchronization (BATS), synchronization iscarried out in order to adjust acquisition timing of sensor signalsamong terminals (TRs) belonging to the area of a base station (BA).

The flowchart of the group analysis when the acquisition time of thesensor signals of plural terminals (TRs) is identical is the same as theflowchart of Embodiment 1 shown in FIG. 5. In the case shown in FIG. 5,the judging functions are specified for each sensing type as shown inFIG. 6. In the case of the present embodiment however, operation can becarried out through the same process. That is, since the sensor signalsof the same clock time can be obtained even when the time of periodicalsensing data sending (SDSF) is different from sampling time (SPT), it ispossible to apply the process of the sensing type 1 (ST1) too.Consequently, the processes of both the sensing type 1 (ST1) and thesensing type 2 (ST2) can be applied. Consequently, detailed explanationsare omitted here.

In Embodiment 2 explained above, it is possible to carry out groupanalysis with a high degree of accuracy even with sensor signals of asmall period of time by synchronizing the acquisition time of sensorsignals of plural terminals (TRs).

1. A group analysis system comprising a plurality of sensor terminalsand a controller connected to said plural sensor terminals, wherein eachof said sensor terminals has a first sensor to acquire relative positioninformation on a person carrying said sensor terminal and a secondsensor to acquire environmental information on said person carrying saidsensor terminal, and wherein said controller has an input-output unit toacquire said relative position information and said environmentalinformation transmitted from said each of said sensor terminals, and ananalyzer to detect an initial group of said sensor terminals with saidrelative information, extract the feature quantity, showing theenvironment of a site, of said initial group from said environmentalinformation on each of said sensor terminals constituting said initialgroup, extract the feature quantity showing the environment of a sitefrom said environmental information on said sensor terminal notbelonging to said initial group, and decide a group to which said sensorterminal not belonging to said initial group belongs by comparing saidfeature quantity of said initial group with said feature quantity ofsaid sensor terminal not belonging to said initial group.
 2. The groupanalysis system according to claim 1, wherein said first sensor is aninfrared-ray sensor; and said analyzer carries out face-to-facedetection between said sensor terminals with sensor signals of saidinfrared-ray sensor when said initial group is detected.
 3. The groupanalysis system according to claim 1, wherein said second sensor is asensor to detect sound, and said analyzer obtains the average of sensorsignals showing the value of sound detected by said sensor terminalsconstituting said initial group as the feature quantity of said initialgroup.
 4. The group analysis system according to claim 1, furthercomprising a display device connected to said controller, and saiddisplay device displays whether said person carrying said sensorterminal belongs to the same group as other person in temporal sequence.5. The group analysis system according to claim 1, further comprising adisplay device connected to said controller, and said display devicedisplays a group of said sensor terminals in distinction from othergroup.
 6. Group analysis equipment to carry out group analysis of aplurality of sensor terminals, said group analysis equipment comprising:an input-output unit to acquire relative position information andenvironmental information transmitted from each of said sensorterminals, and an analyzer to detect an initial group of said sensorterminals with said relative information, extract the feature quantity,showing the environment of a site, of said initial group from saidenvironmental information on each of said sensor terminals constitutingsaid initial group, extract the feature quantity showing the environmentof a site from said environmental information on said sensor terminalnot belonging to said initial group, and decide a group to which saidsensor terminal not belonging to said initial group belongs by comparingsaid feature quantity of said initial group with said feature quantityof said sensor terminal not belonging to said initial group.
 7. Thegroup analysis equipment according to claim 6, wherein said first sensoris an infrared-ray sensor; and said analyzer carries out face-to-facedetection between said sensor terminals with the sensor signals of saidinfrared-ray sensor when said initial group is detected.
 8. The groupanalysis equipment according to claim 6, wherein said second sensor is asensor to detect sound, and said analyzer obtains the average of sensorsignals showing the value of sound detected by the sensor terminalsconstituting said initial group as the feature quantity of said initialgroup.
 9. The group analysis equipment according to claim 6, whereinsensor signals showing said relative position information and saidenvironmental information are the signals sampled for a shorter periodof time than the transmission cycle of said sensor signals transmittedfrom said sensor terminals.
 10. The group analysis equipment accordingto claim 9, wherein said analyzer computes the average of featureamounts at the portions where said sampled said sensor signals overlapwith each other on a temporal axis when said feature quantity of saidinitial group is extracted.